Training Details:    It has been observed in practice, that warming up individual components in the initial phase, followed by a joint training operation provides better stability than training the entire framework at one go. Text recognition network (T), along with rectification network, is trained using ADADELTA optimizer having a learning rate of 1.0 and a batch size of 64. Meanwhile Feedback module \(\mathbf {F}\)  is trained via Adam optimizer with a learning rate 0.001, and gradient clipping at 5. In the warm-up phase, firstly, text recognition and rectification networks are trained from a union of MJSynth and SynthText datasets, for 600K iterations. The rectification network is frozen thereafter. Then, to capture a linguistic prior from text-data, the feedback module \(\mathbf {F}\)  is trained independently from \(\mathcal {G}\)  for 300K iterations, thereby ignoring the \(F_{\mathcal {P}}\)  part (Eqn. REF ). Finally, keeping \(\mathbf {T}\)  fixed, we train the complete feedback module using \(Y\)  (Eqn. REF ), with the same training specifications for 300K iterations. Now, for the joint training (see algorithm REF ), after updating \(\mathbf {T}\) , \(\mathbf {F}\)  is updated with the prediction \(Y\)  from \(\mathbf {T}\) , as input. Thereafter, keeping \(F_{\mathcal {P}}\)  fixed, \(\mathbf {F}\)  is updated using \(\mathcal {G}\) . During this training, we reduce the learning rate of \(\mathbf {T}\)  to 0.01 which continues for 600K iterations. Owing to corresponding data sizes in HTR, iterations for warm-up phases are 100K, 50K and 50K respectively, while for joint training it is 100K. For both STR and HTR, we resize the image to 32x100 and train our model in a 11 GB Nvidia RTX 2080-Ti GPU. \(\lambda _{1}\) , \(\lambda _{2}\) , \(\lambda _{3}\)  and \(\lambda _{4}\)  has been assigned the values of 1, 1, 50 and 0.5 respectively for the same purpose. In order to generate the text-only data \(\mathcal {G}\) , we follow an approach similar to [1]}. The entire training dataset is divided into train-validation split in a cross validation setting, where top ten beam search decoded hypotheses are collected from different state-of-the-art (SOTA) models [2]}, [3]}, [4]}, [5]}, to harness (using open-source code) the error information from closest possible erroneous alternatives. Such a collection may consist of correctly or incorrectly predicted words. This requires the feedback module to learn a language prior for fixing a wrongly predicted word, as well as for a one-to-one character-sequence mapping for correctly predicted words.
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